Precision medicine and data
KidneyIntelX.dkd
AI-enabled in vitro diagnostic test that combines biomarkers and clinical variables to stratify kidney disease progression risk.
Last updated: June 6, 2026
Back to directoryDandelion Health medical AI product profile
Precision medicine and clinical AI platform that pairs multimodal real-world patient data with validated algorithms for research, development, and evidence workflows.
Precision medicine and data
Best fit
Teams that need representative multimodal clinical data, AI-derived labels, algorithm validation, bias assessment, or real-world evidence before development, regulatory, or commercial decisions.
- Primary use case
- Multimodal real-world data, clinical AI marketplace, algorithm validation, bias checks, and regulatory-grade evidence generation
- Audience
- Life-sciences evidence teams, AI developers, clinical researchers, medical device companies, and data science groups
- Risk level
- High
- Pricing signal
- Enterprise data, marketplace, validation, and life-sciences pricing; request current access, study, and developer terms.
- Official sources
- 6 official sources
Compare within workflow: Precision medicine and data · comparison shortlist · source index
Regulatory, privacy, evidence, and workflow lens
Product-specific review. These product-specific signals summarize what the cited sources imply before treating Dandelion Health as safe for a local clinical, operational, or research workflow.
| Regulatory / FDA | Treat as research, validation, and evidence infrastructure unless a project supports a regulated submission or a patient-care workflow; align each study with protocol, SaMD, FDA, sponsor, and IRB expectations. |
|---|---|
| Privacy | Review de-identification, expert-determination claims, data-use agreements, hospital-system source rights, secure-computing controls, exports, retention, and whether any customer-provided data changes HIPAA or contractual obligations. |
| Evidence | Validate cohort definitions, modality coverage, label quality, algorithm performance, subgroup bias, provenance, missingness, and reproducibility before using outputs for development, regulatory, payer, or commercial decisions. |
| Workflow | Best used by clinical research, data science, biostatistics, privacy, regulatory, and domain experts who approve protocols and review AI-derived evidence before action. |
Where Dandelion Health fits
Dandelion Health describes a multimodal precision medicine platform with clinical notes, images, waveforms, EHR data, AI-derived labels, a Clinical AI Marketplace, algorithm validation, bias checks, provenance, and regulatory-grade real-world evidence support.
Not for: Point-of-care diagnosis, autonomous treatment selection, direct patient outreach, or relying on AI-derived real-world evidence without protocol, privacy, statistical, and clinical review.
What to verify before using Dandelion Health
- Which workflow is in scope: Dandelion Data, Clinical AI Marketplace, AI developer validation, life-sciences evidence generation, or a disease-specific data library.
- Data provenance, de-identification method, hospital-system coverage, modality completeness, refresh cadence, and permitted use for the intended research or validation question.
- How marketplace algorithms are validated for performance, bias, demographics, social determinants of health, technologies, sites, and custom cohorts.
- Whether outputs require IRB review, data-use approval, regulatory submission controls, external validation, or expert biostatistical and clinical signoff.
Source links
Use these links to confirm current claims, terms, regulatory status, pricing, and deployment requirements.
- dandelionhealth.ai
- dandelionhealth.ai / platform
- dandelionhealth.ai / clinical-ai-marketplace
- dandelionhealth.ai / news / clinical-ai-marketplace-launch
- dandelionhealth.ai / glp1-real-world-efficacy
- dandelionhealth.ai / sdoh-validation-for-health-equity
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